water

Article Variation Analysis of Streamflows from 1956 to 2016 Along the ,

Xiujie Wang 1,*, Bernard Engel 2, Ximin Yuan 1 and Peixian Yuan 1

1 State Key Laboratory of Hydraulic Engineering Simulation and Safety, University, Tianjin 300350, China; [email protected] (X.Y.); [email protected] (P.Y.) 2 Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47907, USA; [email protected] * Correspondence: [email protected]; Tel.: +86-222-740-1127

 Received: 26 July 2018; Accepted: 7 September 2018; Published: 12 September 2018 

Abstract: With the change of climate and the impacts of human activities, the water resources crisis of the Yellow River is becoming increasingly serious. How and why did the streamflows of the Yellow River basin change? Based on observed annual runoff data (1956–2016) of 10 main hydrological stations along the Yellow River, the linear regression method, the Spearman rank correlation method and the Mann-Kendall test method are used to analyze runoff trend. The orderly clustering method, the sliding t test method and the Lee-Heghinian Method are used to identify the abrupt change point. Finally, the wavelet analysis method is used to identify runoff time series period. The results show that: (1) With the exception of the streamflow of Tangnaihai, the streamflows of all examined stations have significantly declining trends. The decrease of the streamflow from the upper to the middle to the lower reaches is becoming more and more obvious; (2) The runoff of the Yellow River has changed greatly. The abrupt change point at Tangnaihai occurred in 1989. The abrupt change points of the other stations took place in 1985; (3) The runoff along the Yellow River presents multi-time scale changes. The streamflows appear to have strongest periods of 25–40 years with a 40-year scale, which indicate the alternate oscillations of the high and the low water periods. The periods of <6 and 7–24 years are not stable and are complicated. The first main period of runoff in the Yellow River is 30 years; (4) The streamflow upstream of Tangnaihai station is mainly affected by the climate. The streamflows downstream of Tangnaihai station are influenced by human activities, especially water extraction and diversion and the operations of the large reservoirs. These research results have important practical guiding significance for hydrological forecasting, evaluation and management of water resources, construction of water conservancy projects and sustainable utilization of water resources in the region.

Keywords: hydrological time series; the Yellow River; trend analysis; abrupt change point; period identification; wavelet analysis

1. Introduction River runoff is an important water resource. As climate change and human activities intensify, river runoff is undergoing significant spatiotemporal changes. It has direct effects on the allocation, exploitation and utilization of water resources and the physical, chemical and biological processes of river ecosystems [1–4]. Therefore, the attribution analysis of river runoff change has been one of the major and difficult problems in current hydrological science. As the second largest river in China, the Yellow River is the paramount water source in northwestern and northern China [5]. The Yellow River supplies fresh water not only for approximately 114 million people, accounting for about 8.7% of the total Chinese population [6,7], but also for agricultural production, accounting for about 13% of the country’s total cultivated area [8]. As a result

Water 2018, 10, 1231; doi:10.3390/w10091231 www.mdpi.com/journal/water Water 2018, 10, 1231 2 of 20 of population increase, economic development and natural factors, the water resources of the Yellow River are now under great pressure as a water resource [9]. The flow shows a significant decreasing trend [10,11] with the exception of the upstream of the Tangnaihai station [12]. The decrease of flow is mainly influenced by climate and human activities. Many researchers have conducted widely studied on these two aspects, but their results are inconsistent. Ref. [13] found that reduced precipitation in the 1990s accounted for 75% and 43% of the reductions in river discharge in the upper and middle drainage basins. Ref. [1] concluded that during 1970–2008, climate change and human activities respectively contribute 17% and 83% to the reduction in water discharge in the Upper reaches of Yellow River basin. The corresponding relative contributions in the Middle reaches are 71% and 29% to reductions in water discharge. Ref. [14] reported that changes in land use were responsible for more than 70% of the decrease in streamflow of the headwater sub-catchments of the Yellow River basin that occurred in the 1990s. Ref. [15] thought that climate change contributed to total runoff deviations by over 40%, while water consumption and soil erosion control practices contributed over 50% in the middle Yellow River. For the entire runoff change of the whole Yellow River, the total contributions from precipitation were 11.76%, from potential evapotranspiration were 3.83%, and from human activities were 92.07% [16]. The average human activities contribution rate is 72.0% for the streamflow reduction, which is significantly stronger than the precipitation contribution rate (28.0%) [17]. In research methods, the majority of previous studies [17–20] use a single method to study trend and variation of runoff time series, and the results obtained lack a certain degree of feasibility. Therefore, this paper uses multiple methods to evaluate and analyze the change trend and the variation of the entire Yellow River flow from 1956–2016. In addition, the periodic characteristics are identified by using wavelet analysis. The objectives of this study are: (i) to analyze the spatiotemporal variations during the last 60 years in streamflow along the whole Yellow River; (ii) detect both trends and abrupt changes of streamflow; (iii) identify the characteristics of multi-time scales in streamflow; and (iv) discuss the main reasons for the variations in streamflow. The research results of this paper are expected to provide a helpful reference for water resource planning and effective management, the construction of water conservancy projects, and ecological environment protection in the Yellow River basin.

2. Study Area The Yellow River originates from the Yueguzonglie basin in the north of Bayan Har Mountains of Qinghai-Tibet Plateau, covering a drainage area of 795,000 km2, with a total length of 5464 km. Compared to other rivers, one of its strikingly different characteristics is the different sources of flow and sediment. According to its distinct climatic, hydrological, and geomorphological characteristics, the Yellow River is commonly separated into three major sections: The upper, middle, and lower reaches. The Yellow River Basin covers many sub-reaches, but the water is mainly yielded from its upper reach in the Qinghai-Tibetan Mountains [21]. The upstream area is a 3471 km long area from the river head to Toudaoguai which is located in Hekou town in the Autonomous Region with a catchment area of 386,000 km2. The upper reaches of Yellow River basin are in arid and semi-arid areas. The area from river head to Longyangxia Reservoir is located in the high and cold area of Qinghai Plateau which contains alpine steppe, alpine meadow and marsh steppe. The area from Longyangxia to , located in the connecting part of the Qinghai Plateau and the Loess Plateau, mainly contains alpine meadow and meadow steppe, where arable land accounts for only 9.5% of the total area, and woodland area accounts for 14.8%. The area from Lanzhou to Toudaoguai is located in the region of the Loess Plateau, with annual average precipitation of only between 150 and 300 mm [22]. It is adjacent to the of Inner Mongolia in the north and the Erdos desert in the south. The middle reaches pass the Loess Plateau, which is characterized by a fragile ecological environment and severe soil loss [11]. The middle reaches stretch over a length of 1206 km from Toudaoguai to Huayuankou with a basin area of 362,138 km2. This section is mainly located in the Loess Plateau with large numbers of tributaries converging into it. It descends from an altitude of Water 2018, 10, x FOR PEER REVIEW 3 of 20 Water 2018, 10, 1231 3 of 20 Loess Plateau with large numbers of tributaries converging into it. It descends from an altitude of 1000 m at Toudaoguai to 95 m at Huayuankou. The climate is semiarid and arid with an annual 1000 m at Toudaoguai to 95 m at Huayuankou. The climate is semiarid and arid with an annual average precipitation of 516 mm. average precipitation of 516 mm. The lower reaches where elevation is under 95 m are confined by manmade levees on both sides. The lower reaches where elevation is under 95 m are confined by manmade levees on both The basin area is 22,726 km2 and the climate is humid with an annual average precipitation of 648 sides. The basin area is 22,726 km2 and the climate is humid with an annual average precipitation of mm [23]. 648 mm [23].

3.3. MaterialsMaterials andand MethodsMethods

3.1.3.1. DataData SourcesSources DataData used in in this this study study mainly mainly include include the theannual annual runoff runoff (108 m (103)8 observedm3) observed from 1956 from to 1956 2016 toof 201610 hydrologic of 10 hydrologic stations stationsalong the along Yellow the River. Yellow These River. stations These are stations Tangnaihai, are Tangnaihai, Lanzhou, Xiaheyan, Lanzhou, Xiaheyan,Shizuishan, Shizuishan, Toudaoguai, Toudaoguai, Longmen, Longmen, Tongguan, Tongguan, Huayuankou, Huayuankou, Gaocun Gaocun and Lijin and (Figure Lijin (Figure 1). The1). Thedetails details of data of data information information are are shown shown in inthe the Table Table 1.1 Among. Among these these hydrological hydrological stations, Tangnaihai, Lanzhou,Lanzhou, Xiaheyan,Xiaheyan, ShizuishanShizuishan are locatedlocated inin thethe upstreamupstream region.region. TangnaihaiTangnaihai isis thethe inflowinflow stationstation ofof LongyangxiaLongyangxia reservoir,reservoir, andand LanzhouLanzhou isis thethe outflowoutflow station.station. ToudaoguaiToudaoguai stationstation isis locatedlocated inin thethe boundaryboundary areaarea betweenbetween thethe upperupper andand middlemiddle reaches,reaches, whichwhich isis thethe controlcontrol stationstation toto monitormonitor thethe comingcoming flow flow and and sediment sediment from from the upper the upper reaches reaches of the Yellowof the River. Yellow Longmen River. Longmen station and station Tongguan and stationTongguan are locatedstation are in the located middle in reaches.the middle Huayuankou reaches. Huayuankou station is the station boundary is the station boundary between station the middlebetween reaches the middle and the reaches lower and reaches. the lower It is the reaches. starting It is point the starting for the Yellow point for River the becoming Yellow River the Overhangingbecoming the River. Overhanging The Gaocun River. and The Lijin Gaocun stations and belong Lijin to stations the downstream belong to region. the downstream Lijin hydrological region. stationLijin hydrological is the last stationstation onis the Yellowlast station River. on Thethe Yellow calculated River. annual The runoffscalculated by annual daily runoff runoffs time by seriesdaily wererunoff consistent time series with were values consistent of annual with runoff values from of theannual Yellow runoff River from water the resourcesYellow River bulletin water in repeatedresources years. bulletin As such,in repeated two time years. series As are such, reliable two andtime consistent. series are Datareliable of annualand consistent. reservoir storageData of capacityannual reservoir change include storage Liujiaxia capacity reservoir change andinclude Longyanxia Liujiaxia from reservoir 1987 to and 2016 Longyanxia and Xiaolangdi from reservoir 1987 to from2016 1999and Xiaolangdi to 2016. Data reservoir of annual from water 1999 diversion to 2016. Data consumptions of annual include water diversion the upper, consumptions middle and lowerinclude regions the up inper, the middle Yellow and River lower basin regions from in 1998 the to Yellow 2015. TheseRiver basin data comefrom mainly1998 to 2015. from theThese Yellow data Rivercome watermainly resources from the committee Yellow River and partlywater fromresources the Yellow committee River and water partly resources from bulletin,the Yellow which River is compiledwater resources by the Yellowbulletin River, which water is compiled resources by committee. the Yellow River water resources committee.

Figure 1. Location of the Yellow River basin, 10 hydrological stations and large reservoirs. Figure 1. Location of the Yellow River basin, 10 hydrological stations and large reservoirs.

Water 2018, 10, 1231 4 of 20

Table 1. Data information of ten hydrological stations along the Yellow River.

Station Names Area (km2) Longitude Latitude Series Length Time Interval 1956–1987 Daily Tangnaihai 121,972 ◦ 0 ◦ 0 100 09 35 30 1986–2016 Yearly 1956–1987 Daily Lanzhou 222,551 ◦ 0 ◦ 0 103 49 36 04 1986–2016 Yearly 1956–1988 Daily Xiaheyan 254,142 ◦ 0 ◦ 0 105 03 37 27 1986–2016 Yearly 1956–1988 Daily Shizuishan 309,146 ◦ 0 ◦ 0 106 47 39 15 1986–2016 Yearly 1956–2006 Daily Toudaoguai 367,898 ◦ 0 ◦ 0 110 02 40 27 1998–2016 Yearly 1956–2005 Daily Longmen 497,552 ◦ 0 ◦ 0 110 35 35 40 1998–2016 Yearly 1956–2005 Daily Tongguan 682,144 ◦ 0 ◦ 0 110 18 34 36 1998–2016 Yearly 1956–2012 Daily Huayuankou 730,000 ◦ 0 ◦ 0 113 39 34 55 1998–2016 Yearly 1956–2009 Daily Gaocun 734,146 ◦ 0 ◦ 0 115 05 35 23 1998–2016 Yearly 1950–2014 Daily Lijin 751,900 ◦ 0 ◦ 0 118 18 37 31 1998–2016 Yearly The location of the stations can be referred to Figure1.

3.2. Methods Hydrological time series are comprehensive reactions of combined climate conditions, natural geographical conditions and human activities [24]. Thus, the data themselves reflect the influence of these factors. In general, hydrological sequences can always be decomposed into two components, namely deterministic and stochastic components. Deterministic components contain certain physical concepts, including period, trend and jump components. Random components are caused by irregular oscillations and random effects, which cannot be explained strictly on a physical basis. In this paper, evolution trends, abrupt change points and periods of the Yellow River runoff are studied, while not considering stochastic components. For the studies of the trend and the abrupt change point, their theory and methods are mainly based on the statistical parameters and nonparametric test method. The different statistical methods have different assumptions (such as that data must have a normal distribution, homogeneity, etc.). If the data cannot meet the hypothesis condition, the calculated result will deviate from the actual situation [25–28]. Thus, it is assumed that the existence of trend and variation of streamflow sequences should be verified using at least two methods [29]. Identification of dominant periods is a typical and important issue in hydrologic series analysis, because it is the basis of building effective stochastic models and understanding complex hydrologic processes [30]. The estimated spectra using these traditional methods (such as FFT, MESA) often exhibit pseudo-periods, which sometimes cannot identify certain actual periods [24,31]. The EMD method can directly represent multiple time scale change, but because of the intermittent noise signal and the influence of the modal mix, instability in the EMD algorithm makes the physical meaning unclear [24,32]. Wavelet analysis can better realize the time-frequency localization and reveal the implied change characteristics of the time series. Therefore, it is suitable for the study of complex hydrologic systems with multi-time scale variation characteristics [30]. Water 2018, 10, 1231 5 of 20

3.2.1. Trend Analysis Method Trend analysis is an important and common tool for developing a better understanding of the impacts of climate change and human activities on hydrological systems. In order to have more confidence on the existence of trends in streamflow time series, we decided to apply three different tests in this study. The significance level of statistical analysis was uniformly set as magnitude 0.05, so as to avoid analysis error caused by different confidence intervals and make the calculation results comparable.

(1) Linear regression method

The linear regression (LR) trend method is one of the most common mathematical statistics methods, which can directly reflect the qualitative change trend of sequences. Thus, it is widely used in trend analysis of time series of precipitation, runoff, temperature and water quality. The linear regression method is used to judge the trend change by the significance test of the linear correlation coefficient [33–35].

(2) Mann-Kendall test method

The Mann-Kendall (MK) test method is a non-parametric test method recommended by WMO (World Meteorological Organization) and is widely used to analyze the trend of time series of precipitation, runoff, and temperature and water quality. The MK test does not require samples to follow a certain distribution, nor is it subject to the interference of a few abnormal values. It is applicable to data of non-normal distributions such as hydrology and meteorology. The MK test method has the characteristics of simple calculation, wide detection range and a high degree of quantification [25,35–38].

(3) Spearman rank correlation method

The Spearman rank correlation method is a robust and simple test method, which does not require certain distribution of samples and is not disturbed by a few abnormal values. This method is not a function of time sequence itself, but the information of the observed value rank (i.e., number of qualifying) function. Thus, it can eliminate the influence of the distribution of the original sequence. It was found that the Spearman rank test is more concise than MK. The Spearman rank test principle is based on the correlation coefficient test [25,34,35,37,39,40].

3.2.2. Identification of Abrupt change Point Change point analysis is one important element of hydrological statistics and analysis, especially in the distinction between the impact of climate change and human activities on the hydrological processes [41,42]. Compared with the trend, the change point inference is more complex, and the time and amplitude of the jump may reach different conclusions due to different test methods [43]. Therefore, this paper adopts three methods: The sliding t test, the ordered clustering method and the Lee-Heghinian method.

(1) Sliding t test method

The sliding t test method verifies the difference of two average values by checking whether there is a significant difference in the mean of the two sub-sequences in a sequence. If the difference of means of the two sub-sequences exceeds a certain significance level, it means that sequence variation has occurred [44]. The traditional t test is a parameter method. The corresponding point of the maximum value is found to be the abrupt change point only through finding all points satisfied the given conditions [2].

(2) Clustering ordered method

The ordered cluster analysis is a method of classifying samples according to the size of the sample gap in a continuous sample. Its mathematical theory is based on the classification algorithm proposed by Fisher, also known as the optimal segmentation method. The principle of classification is to minimize the sum of squares between the same class. The sums of squares between two classes are the maximum [2,38]. Water 2018, 10, 1231 6 of 20

(3) Lee-Heghinian Method

The Lee-Heghinian method is a variable point test method based on Bayesian theory proposed by Lee and Heghinian in 1977. When the posterior conditional probability density reaches the maximum value, the corresponding variable value is the most likely variable point [45,46].

3.2.3. Period Identification of Wavelet Analysis In practical research, the wavelet transform coefficients are obtained through the wavelet transform equation, by which the time and frequency characteristics of time series are analyzed. Selecting the proper fundamental wavelet function is the precondition of wavelet analysis [33]. In this paper, the selected Morlet complex wavelet function is a single frequency complex sine function under Gaussian envelope, because its time and frequency domain localization is better [47]. Based on the results of time scale distribution, it is easy to analyze the periodicity of streamflow 2 series. For time series f (t) ∈ L (R), Wf (a, b) is the result of the continuous wavelet transform (CWT) [47], i.e., −1 Z +∞  t − b  Wf (a, b) = |a| 2 f (t) dt (1) −∞ a

Wf (a, b) is the wavelet coefficient, which varies with parameters a and b. The Morlet complex wavelet function Ψ is expressed as follows [33]:

2 Ψ(t) = eiω0te−t /2 (2) where ω0 is a constant; i is the imaginary number. The relationship between time scale a and period T of the Morlet complex wavelet follows: a T = (3) Fc × Fs where Fc is the central frequency of the wavelet; Fs is the sample frequency. The square integral of all wavelet coefficients on scale a in the time domain is the wavelet squared difference: Z ∞ 2 Var(a) = Wf (a, b) db (4) −∞ In the process of wavelet analysis, due to the limitation of the observation conditions, the measured length of annual runoff series is limited, so on both ends of the sequence so-called “boundary effects” possibly occur, especially in shorter sequences. In order to avoid this deficiency, both ends of the hydrological time series were extended symmetrically in this paper.

4. Results

4.1. Change Process of Annual Streamflow Figure2 presents the annual runoff change processes of the four stations in the upper reaches of the Yellow River, namely Tangnaihai, Lanzhou, Xiaheyan and Shizuishan. It can be seen that the annual runoff change processes of Lanzhou, Shizuishan and Toudaoguai (Figure3) are consistent. The change process of Tangnaihai is different from those of the other three stations. The annual streamflow from Tangnaihai to Lanzhou increases, while the annual streamflow from Lanzhou to Xiaheyan, Shizuishan and Toudaoguai decreases. Figure3 presents the annual runoff variation trends of Toudaoguai, Longmen and Tongguan in the middle reaches of the Yellow River. It can be seen that the annual runoff change of Toudaoguai is consistent with that of Longmen. The annual runoff changes at Tongguan and Huayuankou (Figure4) are consistent. The annual flow from Toudaoguai to Longmen, Tongguan and Huayuankou increases along the way. Water 2018, 10, x FOR PEER REVIEW 7 of 20 Water 2018Water, 10 2018, 1231, 10, x FOR PEER REVIEW 7 of7 of20 20 Water 2018, 10, x FOR600 PEER REVIEW Tangnaihai 7 of 20 550 600 LanzhouTangnaihai 600 550 XiaheyanTangnaihaiLanzhou 500 Shizuishan ) LanzhouXiaheyan 3 550 500 linear fitting of Tangnaihai

m Shizuishan ) Xiaheyan 8

4503 500 linearShizuishanlinear fitting fitting of Lanzhou of Tangnaihai ) m 3 8 450 400 linearlinearlinear fitting fitting fitting of of Xiaheyan Tangnaihaiof Lanzhou m

8 450 linearlinearlinear fitting fitting fitting of of Shizuishan Lanzhouof Xiaheyan 350 400 400 linearlinear fitting fitting of Xiaheyanof Shizuishan 300 350 linear fitting of Shizuishan 350 250 300 300 200 250

Water discharge (10 250 150 200 200Water discharge (10

100Water discharge (10 150 150 19551001960196519701975198019851990199520002005201020152020 100 19551960196519701975Year198019851990199520002005201020152020 1955196019651970197519801985Year1990199520002005201020152020 Figure 2. Variation trend of annual streamflowYear of the upper reaches in the Yellow River.

Figure 2. Variation trend of annual streamflow of the upper reaches in the Yellow River. Figure 2. Variation trend of annual streamflow of the upper reaches in the Yellow River. 800Figure 2. Variation trend of annual streamflow of the upper reaches in the Yellow River. 800 Toudaoguai 700800 Toudaoguai ) Longmen 3 700 Toudaoguai m ) Longmen 8 3 Tongguan 600700 ) m Longmen 3 8 Tongguan 600 linear fitting of Toudaoguai m

8 Tongguan 500600 lienarlinear fitting fitting of Longmen of Toudaoguai linear fitting of Toudaoguai 500 linearlienar fitting fitting of Tongguan of Longmen 400500 lienarlinear fitting fitting of Longmenof Tongguan 400 linear fitting of Tongguan 300400

Water discharge (10 300 300

200Water discharge (10

Water discharge (10 200 100200 1955100 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 100 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 Year 1955 1960 1965 1970 1975 1980 1985 Year1990 1995 2000 2005 2010 2015 2020 Figure 3. Variation trend of annual streamflow of the middle reaches in the Yellow River. Year FigureFigure 3. Variation 3. Variation trend trend of annual of annual streamflow streamflow of of the the middle middlereaches reaches in the Yellow Yellow River River.. Figure 3. Variation trend of annual streamflow of the middle reaches in the Yellow River. 1100 Huayuankou 10001100 1100 GaocunHuayuankou

) 1000 3 900 LijinHuayuankouGaocun m

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3 900 linear fitting of Gaocun 7008 800 Lijinlinear fitting of Huayuankou m linear fitting of Lijin 8 800 linearlinear fitting fitting of Huayuankouof Gaocun 600 700 linearlinear fitting fitting of Gaocunof Lijin 700 harge (10 500 600 linear fitting of Lijin

600harge (10 400 500

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100Water disc 200

Water disc 200 0 100 10019550 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 0 1955 1960 1965 1970 1975 1980Year1985 1990 1995 2000 2005 2010 2015 2020

1955 1960 1965 1970 1975 1980 1985 1990Year1995 2000 2005 2010 2015 2020 FigureFigure4. 4. VariationVariation trend trend of of annual annual streamflow streamflow ofYear the of thelower lower reaches reaches in the in Yellow the Yellow River.River.

Figure 4. Variation trend of annual streamflow of the lower reaches in the Yellow River. Figure4 Figurepresents 4. Variation the annual trend of runoffannual streamflow variation of trends the lower of reaches Huayuankou, in the Yellow GaocunRiver. and Lijin hydrological stations. In general, the annual runoff changes of the three stations are consistent. Especially before 1970 (except 1960), the runoff changes of the three stations were nearly identical. After 1970, annual streamflow from Huayuankou to Gaocun and Lijin decreased along the way. Water 2018, 10, 1231 8 of 20

4.2. Trend Change and Abrupt Change Points The trend evaluation and level tests were carried out by using the linear regression method, the Mann Kendall rank method and the Spearman rank correlation method under a confidence level of 0.05. The runoff decrease trend of Tangnaihai was not significant. The significance of annual runoff decrease trend of Lanzhou station had mixed results. One result was a significant trend by the linear regression test (Figure2) and the Spearman’s rank test. Another result was not significant by the Mann-Kendall rank method. For the remaining 8 stations (Xiaheyan, Shizuishan, Toudaoguai, Longmen, Tongguan, Huayuankou, Gaocun and Lijin), the test results of three methods are a significantly decreasing trend. In identifying the abrupt change point, the T sliding method, the Lee-Heghinian method and the Orderly clustering method were used. The results of three methods are consistent for each station. The abrupt change point of Tangnaihai is in 1989, and for the remaining 9 stations, the abrupt change points are all in 1985 (Table2).

Table 2. Trends of annual streamflow of ten stations along the Yellow River from 1956 to 2016.

Station Name LR T(0.05/2) = 1.64 MK U(0.05/2) = 1.96 Spearman T(0.05/2) = 1.64 Trend Abrupt Change Point Tangnaihai −1.64 −0.99 −1.05 Decreasing 1989 Lanzhou −2.49 −1.82 −1.89 Decreasing 1985 Xiaheyan −2.89 −2.36 −2.6 Decreasing 1985 Shizuishan −3.64 −3.21 −3.6 Decreasing 1985 Toudaoguai −3.99 −3.63 −3.99 Decreasing 1985 Longmen −5.75 −5.06 −6.53 Decreasing 1985 Tongguan −6.65 −5.59 −7.72 Decreasing 1985 Huayuankou −5.96 −5.26 −6.52 Decreasing 1985 Gaocun −5.98 −5.25 −6.49 Decreasing 1985 Lijin −6.84 −5.79 −7.45 Decreasing 1985

The average and the variation coefficient (CV) are the two most important parameters for hydrological design of water conservancy projects, which affect the scale and safety of water conservancy projects. Moreover, CV is also the main index reflecting the interannual change of river runoff. At the abrupt change point (excluding variation points), the whole time series are divided into two subsequences. Then the average and CV of the whole sequence, before variation and after variation sequences are calculated. The results are shown in Table3 indicating that the mean annual runoff before the variation sequence in each station is largest. The average after the variation sequence in each station is smallest. From the upstream to the middle and the lower reaches, the change of the average is increasingly large. Compared with the average of the subsequence before variation, the streamflow of Tangnaihai, Toudaoguai and Lijin decreases to 86%, 66% and 38%, respectively. In general, the order of CV is the whole sequence, the sequence before variation and the subsequence after variation. From the upper to the middle and the lower reaches, the CV of the sequences (except Lanzhou) are larger and larger.

Table 3. Average and CV of different flow time series along the Yellow River.

Average of Different Flow Series (108 m3) CV of Different Flow Series Station Name 1956–2016 1956–1984 1986–2016 1956–2016 1956–1984 1986–2016 Tangnaihai * 199.02 210.16 181.09 0.26 0.24 0.24 Lanzhou 305.32 335.22 274.98 0.22 0.22 0.16 Xiaheyan 291.05 326.24 255.64 0.24 0.23 0.19 Shizuishan 266.54 306.20 226.40 0.28 0.25 0.21 Toudaoguai 205.31 247.79 162.57 0.36 0.30 0.27 Longmen 249.50 307.24 192.37 0.35 0.27 0.24 Tongguan 324.38 407.51 242.60 0.37 0.27 0.24 Huayuankou 354.98 450.28 260.99 0.41 0.32 0.25 Gaocun 331.02 431.02 232.81 0.46 0.35 0.29 Lijin 275.05 401.55 152.91 0.66 0.44 0.45 * Flow sequences time range of Tangnaihai station divided by abrupt change point are 1965–1988 and 1990–2016. Water 2018, 10, x FOR PEER REVIEW 9 of 20

Tongguan 324.38 407.51 242.60 0.37 0.27 0.24 Huayuankou 354.98 450.28 260.99 0.41 0.32 0.25 Gaocun 331.02 431.02 232.81 0.46 0.35 0.29 Lijin 275.05 401.55 152.91 0.66 0.44 0.45 * Flow sequences time range of Tangnaihai station divided by abrupt change point are 1965–1988 and Water 2018, 10, 1231 9 of 20 1990–2016.

4.3.4.3. Period Period is is an an important important component component of runoff of runoff compositions. compositions. Wavelet Wavelet transform transform was used was to usedanalyze to theanalyze periods the ofperiods flow along of flow the along Yellow the River. Yellow The River. streamflows The streamflows of Tangnanhai, of Tangnanhai, Toudaoguai Toudaoguai and Lijin were and selectedLijin were to selected carry out to the carry analysis out the in analysis detail. in detail. (1) The real part of the wavelet coefficient (1) The real part of the wavelet coefficient The real part of the wavelet coefficients can reflect runoff series periodic change at different time scalesThe and real the part distribution of the wavelet in the coefficients time domain, can reflect and runoffthen can series judge periodic the future change trend at different of flow time series scales at anddifferent the distribution time scales. in Wh theen time the domain, real part and of then the canwavelet judge coefficient the future trendis positive, of flow it series shows at that different runoff time is scales.in a high When flow the period. real part If the of the real wavelet part of coefficient the wavelet is positive, coefficient it shows is negative, that runoff it denotes is in a highthat flowrunoff period. is in Ifa low the realflow part period. of the wavelet coefficient is negative, it denotes that runoff is in a low flow period. From the the real real part part coefficients coefficients contour contour map map of ofTangnaihai Tangnaihai (Figure (Figure 5),5 it), can it can be clearly be clearly seen seen that thatthere there are 3 are–6, 6 3–6,–24 6–24and 25 and–37 25–37 years years periodical periodical characters characters within within a 40-year a 40-year scale. scale.The scales The scalesof 25–37 of 25–37years were years very were stable very in stable the whole in the wholetime domain. time domain. There were There 3 high were water 3 high and water 2 low and water 2 low shocks. water shocks.On 6–24 Onand 6–243–6-year and scales, 3–6-year their scales, oscillation their oscillationfrequencies frequencies are very high are and very complicated. high and complicated. The scale of The3–6 years scale ofoccurred 3–6 years during occurred 1975– during1998. 1975–1998. For Toudaoguai station ( (FigureFigure 66),), there are three three time time scale scale changes. changes. Among Among them, them, the the scales scales of of20– 20–4040 and and 5–23 5–23 years years areare stable stable in the in thewhole whole time time domain. domain. The Theformer former experienced experienced 3 high 3 high water water and and2 low 2 lowwater water shocks, shocks, and andthe later the later underwent underwent 4 high 4 high water water and and5 low 5 lowwater water shocks. shocks. The Thescale scale of 2– of6 2–6years years is not is stable not stable and andoccurred occurred during during 1971 1971–2002.–2002.

40

75 35 70 60 30 50 40

) 25 r 30 a e

y 20 ( 20 e

l 10 a

c -10 S 15 -20 -30 10 -40 -50 5 -60 -70 0 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 Year Figure 5 5.. RealReal part part coefficients coefficients contour map of streamflow streamflow at Tangnaihai station.station.

Water 2018, 10, 1231 10 of 20 Water 2018, 10, x FOR PEER REVIEW 10 of 20

40 100 90 35 80 70 30 60 50 ) )

r 40

r 40 a a 25

e 30

e 30 y y ( ( 20

e e

l 10 l 20 10 a a

c -10 c -10 S S -20 15 -30 -40 -50 10 -60 -70 5 -80 -90 -100 0 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 Year Figure 6 6.. RealReal part part coefficients coefficients contour map of streamflow streamflow at Toudauguai station.station.

For Lijin Lijin station station (Figure (Figure7), we 7), can we clearly can clearly see the see multi-time the multi scale-time characteristics scale characteristics of flow evolution. of flow Amongevolution. them, Among the scale them, of 25–40the scale years of was 25– very40 years stable. was There very were stable. 3 high There water were and 3 2high low water water and oscillation 2 low periodswater oscillation in the whole periods time in domain. the whole The time scale domain. of 12–24 The years scale was of 12 not–24 stable. years Differentwas not stable. high-low Different water oscillationshigh-low water occurred oscillations in different occurred time domains. in different The time scale domains. of 3–10 The years scale occurred of 3–10 during years 1964–2004. occurred Thereduring were 1964 more–2004. cycle There oscillations. were more cycle oscillations.

40

130 35 120 110 30 90 70 ) ) 25 r r 50 a a e e

y 30 y 30 ( (

20 e e 10

l 10 l a a c c -10 S S 15 -30 -50 10 -70 -90 5 -110 -120 0 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 Year Figure 7 7.. Real part coefficients coefficients contour map of streamflow streamflow at Lijin station.station.

(2) Modulus square of wavelet coefficientscoefficients The modulus square of the wavelet coefficients coefficients represents the energy spectrum. It can reflect reflect the energy variability in time scale. The higher the energy is,is, thethe stronger thethe periodicperiodic oscillationoscillation is.is. In the wavelet modulus square contour map of Tangnaihai (Figure 8), the scale of 26–35 years has the strongest energy and shows the most significant period. The energy of the scale of 19–22 years

Water 2018, 10, 1231 11 of 20

WaterIn 2018 the,wavelet 10, x FOR modulusPEER REVIEW square contour map of Tangnaihai (Figure8), the scale of 26–35 years11 of 20 has the strongestWater 2018, 10 energy, x FOR PEER and REVIEW shows the most significant period. The energy of the scale of 19–2211 of 20 years is stronger,is stronger, which which occurred occurred from from 19701970 to 2002. 2002. The The energy energy of of the the scale scale of 9 of-21 9-21 years years is weak, is weak, which which is is stronger, which occurred from 1970 to 2002. The energy of the scale of 9-21 years is weak, which is mainly concentrated in the two periods of 1956–1970 and 2004–2016. The energy of the scale of 2–7 is mainlymainly concentrated concentrated in the the two two periods periods of of1956 1956–1970–1970 and and2004 2004–2016.–2016. The energy The energy of the scale of the of scale2–7 of years is weaker, which occurred mainly in the period 1968–2004. 2–7 yearsyears is weaker, which which occurred occurred mainly mainly in the in theperiod period 1968 1968–2004.–2004. 40 40 5400 5400 35 5200 35 5200 5000 5000 4800 4800 30 4600 30 4600 44004400 42004200 )

25) r 25 r 4000

a 4000 a e e 38003800 y y ( (

3600 3600

e 20

e 20 l l 34003400 a a c c 32003200 S S 1515 30003000 28002800 26002600 1010 24002400 22002200 20002000 5 5 18001800 16001600 1400 0 0 1400 19551955 19601960 19651965 19701970 19751975 19801980 1985 1990 19951995 20002000 20052005 20102010 20152015 Year Year Figure 8. Modulus square of wavelet coefficient contour map of streamflow at Tangnaihai station. FigureFigure 8. 8Modulus. Modulus square square of of waveletwavelet coefficientcoefficient c contourontour map map of of streamflow streamflow at at Tangnaihai Tangnaihai station station..

For Toudaoguai (Figure 9), the energy of the scale of 25–35 years is the strongest and the period ForFor Toudaoguai Toudaoguai (Figure (Figure9), the9), the energy energy of the of the scale scale of 25–35 of 25– years35 years is the is the strongest strongest and and the periodthe period is the is the most significant, which occurred in the whole time domain. However, the energy of the scales mostis the significant, most significant, which occurred which occurred in the whole in the time whole domain. time However,domain. However, the energy the of theenergy scales of ofthe 14–19 scales and of 14–19 and 3–6 years is weak, and the periodic change is localized. The scale of 14–19 years is mainly 3–6of years 14–19 is and weak, 3–6 andyears the is weak, periodic and change the periodic is localized. change The is localized. scale of 14–19The scale years of is14 mainly–19 years focused is mainly from focused from 1956 to 1976 and from 1995 to 2016. The scale of 3–6 years was mainly concentrated in 1956focused to 1976 from and 1956 from to 1995 1976 to and 2016. from The 1995 scale to 2016. of 3–6 The years scale was of mainly3–6 years concentrated was mainly inconcentrated the period fromin the period from 1978 to 1995. 1978the to period 1995. from 1978 to 1995. 40 40 9500 35 90009500 35 85009000 30 80008500 30 75008000 )

r 25 70007500 a ) e r 25 6500 y 7000 a (

e

e 60006500 y 20 l (

a 5500 c e 20 6000 l S a 50005500 c 15

S 45005000 15 4000 10 4500 35004000 10 3000 3500 5 2500 3000 5 2000 2500 0 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2000 0 Year 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 Figure 9. Modulus square of wavelet coefficientYear contour map of streamflow at Toudaoguai station. FigureFigure 9. 9Modulus. Modulus square square of of waveletwavelet coefficientcoefficient contour map map of of streamflow streamflow at at Tou Toudaoguaidaoguai station station..

Water 2018, 10, 1231 12 of 20 Water 2018, 10, x FOR PEER REVIEW 12 of 20

For Lijin (Figure 1010),), the energy of the scale of 26 26–36–36 years was the strongest and the periods are the most significant, significant, which occurred inin the whole time domain. In the scale of 14 14–22–22 yea years,rs, the the energy energy is stronger and the period is significant, significant, which occurred for the whole period. On On the the scale scale 6 6–10–10 years, years, the energy is weak and the period is not significant, significant, which is mainly concentrated in the periods from 1956 to 1973 and 2002 to 2016.

40

16000 35 15000

30 14000 13000 )

r 25 12000 a e

y 11000 ( 20 e

l 10000 a c

S 9000 15 8000

10 7000

6000 5 5000

4000 0 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 Year Figure 10 10.. ModulusModulus square square of wavelet coefficient coefficient contour map of streamflow streamflow at Lijin station.station.

(3) Wavelet variance The wavelet wavelet square square can can reflect reflect the the distribution distribution of fluctuationof fluctuation energy energy with with scale scale in flow in timeflow series. time series.It can beIt can used be to used determine to determine the main the period main period of flow of evolution. flow evolution. As FiguresFigure 11–13 show, thethe corresponding corresponding period period of of the the maximum maximum values values of each of each station station is 30 is year, 30 whichyear, which indicates indicates the oscillation the oscillation in this in period this period are very are strong, very strong, and are and the are first the main first period main ofperiod annual of annualflow evolution flow evolution in the Yellow in the River Yellow basin. River The b secondasin. The main second periods main of Lanzhou,periods of Xiaheyan, Lanzhou, Toudaoguai, Xiaheyan, Toudaoguai,Longmen and Longmen Tongguan and are Tongguan 15.33 years. are The 15.33 second years. main The periods second mainof Tangnaihai, periods of Huayuankou, Tangnaihai, Huayuankou,Gaocun and Lijin Gaocun are 20.67 and Lijin years. are The 20.67 second years. main The second period main of Shizuishan period of isShizuishan 6 years. The is 6 thirdyears. main The periodsthird main of Tangnaihai,periods of Tangnaihai, Shizuishan Shizuishan and Lijin are and 15.33 Lijin years, are 15.33 and years, the third and main the third periods main of periods Xiaheyan, of ToudaoguaiXiaheyan, Toudaoguai and Longmen and are Longmen 6 years. are The 6 thirdyears. main The periodsthird main of Huayuakou periods of Huayuakou and Gaocun and are 16Gaocun years. Theare 16 third years. main The period third main of Lanzhou period of is Lanzhou 20.67 years. is 20.67 The years. third The main third period main of period Tongguan of Tongguan is 8 years. is The8 years. fourth The main fourth periods main of periods Huayuankou, of Huayuankou, Gaocun andGaocun Lijn and are 8Lijn years. are 8 The years. fourth The main fourth periods main periodsof Xiaheyan, of Xiaheyan, Toudaoguai Toudaoguai and Tongguan and Tongguanare 20.67 years. are 20.67 The fourth years. main The periodfourth ofmain Tangnaihai period of is Tangnaihai5.33 years. Theis 5.33 fourth years. main The period fourth of main Lanzhou period is of 6.0 Lanzhou years. The is 6.0 fourth years. main The period fourth of main Shizuishan period of is 10Shizuishan years. The is fourth10 years. main The period fourth of main Longmen period is of 3.33 Longmen years. is 3.33 years.

Water 2018, 10, x FOR PEER REVIEW 13 of 20

Water 2018, 10, x FOR PEER REVIEW 13 of 20

8 )

Water 2018, 10, x FOR 5 PEER REVIEW 13 of 20

Water 2018, 10, 1231 10 Tangnaihai 13 of 20

× 68 )

5 Lanzhou

10 XiaheyanTangnaihai × 486 ) ShizuishanLanzhou 5

10 ToudaoguaiTangnaihaiXiaheyan

× 264 LanzhouShizuishan XiaheyanToudaoguai Wavelet Wavelet variance ( 2 04 Shizuishan 0 5 10 15Toudaoguai20 25 30 35 40 45 Wavelet Wavelet variance ( 20 Period (year)

0 5 10 15 20 25 30 35 40 45

Figure 11. Wavelet Wavelet variance ( variances of streamflows at Tangnaihai, Lanzhou, Xiaheyan, Shizuishan and 0 Period (year) Toudauguai in the upstream reaches. 0 5 10 15 20 25 30 35 40 45 Figure 11. Wavelet variances of streamflows at Tangnaihai, Lanzhou, Xiaheyan, Shizuishan and Period (year) Toudauguai in the upstream reaches. FigureFigure 11 11.. WaveletWavelet variances variances of of streamflows streamflows at at Tangnaihai, Tangnaihai, Lanzhou, Lanzhou, Xiaheyan, Xiaheyan, Shizuishan Shizuishan and and 15 ) ToudauguaiToudauguai5 in in the the upstream upstream reach reaches.es. 10 12 × 15 )

5 Longmen

10 Tongguan 129 × 15 Huayuankou ) Longmen 5 6 Tongguan 10 9 12

× Huayuankou Longmen 36 9 Tongguan Wavelet Wavelet variance ( 0 3 Huayuankou 6 0 5 10 15 20 25 30 35 40 45 Wavelet Wavelet variance ( 0 Period (year) 3 0 5 10 15 20 25 30 35 40 45

Figure 12. Wavelet Wavelet variance ( 0 variances of streamflows at Longmen,Period (year) Tongguan and Huayuankou in the middle reaches. 0 5 10 15 20 25 30 35 40 45 Figure 12. Wavelet variances of streamflows at Longmen, Tongguan and Huayuankou in the Figure 12. Wavelet variances of streamflows at Longmen, Tongguan and Huayuankou in the middle middle reaches. Period (year) reaches. Figure 12. Wavelet15 variances of streamflows at Longmen, Tongguan and Huayuankou in the middle ) 5 reaches. 10 12 × 15 )

5 Gaocun

10 9 12 Lijin × 15 ) Gaocun 5 6 9 Lijin 10 12 × 3 Gaocun

velet variance ( 6 9 Lijin

Wa 0 3 velet variance ( 6 0 5 10 15 20 25 30 35 40 45

Wa 0 Period (year) 3

velet variance ( 0 5 10 15 20 25 30 35 40 45 Figure 13. Wavelet variances of streamflows at Gaocun and Lijin in the downstream reaches. Figure 13Wa . Wavelet0 variances of streamflows atPeriod Gaocun (year) and Lijin in the downstream reaches. 5. Discussion 0 5 10 15 20 25 30 35 40 45 Figure 13. Wavelet variances of streamflows atPeriod Gaocun (year) and Lijin in the downstream reaches. 5.1. Analysis of Trend TheFigure decrease 13. Wavelet in precipitation, variances of streamflows incoming waterat Gaocun from and the Lijin upper in the downstream reaches, regulation reaches. of large reservoirs, soil and water conservation measures as well as water consumption contribute most to the significant reduction of streamflow. These factors are discussed in the sections that follow.

Water 2018, 10, x FOR PEER REVIEW 14 of 20

5. Discussion

5.1. Analysis of Trend The decrease in precipitation, incoming water from the upper reaches, regulation of large

Waterreservoirs,2018, 10 ,soil 1231 and water conservation measures as well as water consumption contribute most14 of 20to the significant reduction of streamflow. These factors are discussed in the sections that follow.

5.1.1.5.1.1. Meteorological and Climatic Factors Precipitation and and evapotranspiration evapotranspiration are are the the most most important important factors factors to affect to runoffaffect runoffchange change [37,48]. Evaporation[37,48]. Evaporation is affected is affected by temperature by temperature changes. changes. In thethe upstream of LanzhouLanzhou region,region, thethe precipitationprecipitation diddid notnot significantlysignificantly increaseincrease oror decreasedecrease since 1950. From Lanzhou to Toudaoguai, precipitation decrease is relatively obvious, butbut the section waterwater supply accountsaccounts for only 1% of the total water in the main river. Thus, flow flow in the Yel Yellowlow River upstream is not significantly significantly affected by the interannual variability of precipitation in this region region [ [3838]].. The annual air temperature has increased by 0.80 ◦°CC in the headwater catchment of the Yellow River basin during the past 42 yearsyears [[4343].]. TheThe loss of flowflow duedue toto increasingincreasing temperaturetemperature exceededexceeded the slightlyslightly increasedincreased precipitationprecipitation over over the the upper upper Basin Basin [49 [49].]. Ref. Ref. [50 [50] indicated] indicated that that the the variations variations of regionalof regional precipitation precipitation have have a predominant a predominant effect effect on on the the flow flow variations. variations. In the middle reaches of the Yellow River, the average annual rainfall decreased, with an average decrease rate of 2828 mm/amm/a [ [4949]].. Figure Figure 1414 shows correlation diagram of precipitation and streamflowstreamflow inin the middle middle of of the the Yellow Yellow River River from from 1950s 1950s to to 2000s 2000s and and data data in inthe the Figure Figure were were extracted extracted from from the theRef. Ref. [17]. [17 Correlation]. Correlation coefficient coefficient of precipitation of precipitation and and streamflow streamflow is over is over 0.9 0.9in the in the mid middledle reaches reaches of ofthe the Yellow Yellow River, River, which which indicate indicatess that that precipitation precipitation is is an an important important factor factor influencing influencing streamflow streamflow.. The average average annual annual temperature temperature increases increases by 0.3 by ◦ 0.3C every °C every ten years, ten years, and climate and climate warming warming will increase will potentialincrease potential evaporation evaporation and reduce and runoff reduce [51 ,52 runoff]. Strong [51,52 evaporation]. Strong evaporation and drought and were drought exacerbated were byexacerbated the significant by the warming significant and warming precipitation and reductionprecipitation overall reduction and led overall to severe and water led to scarcity severe inwater the middlescarcity basin.in the Climatemiddle basin impacts. Climate on runoff impacts in the on lowerrunoff basin in the of lower the Yellow basin of River the couldYellow be River negligible could becausebe negligible of its because very small of its area very [49 small]. area [49].

Figure 14.14. CorrelationCorrelation diagram diagram of of precipitation precipitation and and streamflow streamflow in thein the middle middle of the of Yellowthe Yellow River River from 1950sfrom 1950s to 2000s. to 2000s. 5.1.2. Human activities

Among all anthropogenic activity factors, consumptions of agriculture and industry, dam construction and water conservation measures have had the greatest effect on the river’s hydrological cycle. Water 2018, 10, x FOR PEER REVIEW 15 of 20

5.1.2. Human activities Among all anthropogenic activity factors, consumptions of agriculture and industry, dam Water 2018, 10, 1231 15 of 20 construction and water conservation measures have had the greatest effect on the river’s hydrological cycle. (1)(1) EffectEffect ofof consumptionconsumption WithWith the the rapid rapid development development of China’sof China national’s national economy, economy, especially especially after the after 1980s, the in1980s, the central in the andcentral western and western regions, regions, agricultural agricultural irrigation, irrigation, industrial industrial production production and urban and water urban demand water demand for the Yellowfor the RiverYellow water River resources water resources grew rapidly. grew rapidly. The annual The diversion annual diversion water from water the from Yellow the River Yellow is aboutRiver 6.401is about billion 6.401 m billion3, accounting m3, accounting for 38% for of 38% the waterof the fromwater the from middle the middle reaches reaches of the of Yellow the Yellow River River [17]. From[17]. From the Figure the Figure 15, it may15, it see may that see water that diversionwater diversion consumptions consumptions of the Upperof the regionUpper areregion the largestare the andlargest the and downstream the downstream region region is the secondis the second largest. largest The. middle The middle stream stream region region is the is smallest the smallest in the in 1998–2015the 1998–2015 period. period. The The trends trends of of water water diversion diversion consumptions consumptions are are increasing increasing in in the the middlemiddle andand downstreamdownstream regions.regions. AccordingAccording toto thethe YellowYellow RiverRiver conservancyconservancy commissioncommission (1998–2012),(1998–2012), statisticsstatistics showshow thatthat agriculturalagricultural irrigationirrigation water consumption is the largest; the second is industrial water, countycounty livestocklivestock andand fishfish farming,farming, residents,residents, andand ecologicalecological waterwater forfor thethe environmentenvironment[ 53[53].].

FigureFigure 15.15. VariationsVariations of water water diversion diversion consumes consumes at at the the upper upper,, middle middle and and lower lower of of along along the YellowYellow River.River. (2) Effect of reservoirs and dams (2) Effect of reservoirs and dams AmongAmong allall humanhuman activities,activities, thethe constructionconstruction of damsdams andand reservoirsreservoirs isis thethe mostmost directdirect wayway toto manipulatemanipulate riverriver waterwater resources.resources. ToTo meetmeet thethe demanddemand ofof agriculturalagricultural irrigation,irrigation, asas wellwell asas toto preventprevent flooding,flooding, thethenatural natural seasonal seasonal distribution distribution of of water water discharge discharge was was changed changed by by dams dams and and reservoirs reservoirs [6]. 3 Longyangxia[6]. Longyangxia reservoir reservoir has has a regulated a regulated capacity capacity of of 19.36 19.36 billion billion m m,3 and, and a a normal normal water water levellevel ofof 3 24.7024.70 billionbillion mm3 [54].]. The reservoir reservoir’s’s storage varies greatly from year to year, with a maximum annual 3 3 waterwater storage storage ofof 9.3 9.3 billion billion m m3 (2005)(2005) and and a a minimum minimum of of 272 272 million million m m3 (Figure(Figure 1616).). TheThe annual annual streamflowsstreamflows below below LanzhouLanzhou areare thereforetherefore greatlygreatly controlledcontrolled byby thethe LiujiaxiaLiujiaxia and and Longyangxia Longyangxia reservoirs.reservoirs. Xiaolangdi Xiaolangdi reservoir reservoir in in the the middle middle reaches reaches which which was was built built in 1999 in 1999has influence has influence on water on 3 waterresources resources of the oflower the lowerreaches. reaches. The maximum The maximum annual annualwater storage water storageis 5.5 billion is 5.5 m billion3 (2003) m (Figure(2003) 4 8 3 (Figure16). The 16 evaporation). The evaporation and leakage and losses leakage from losses the reservoir from the were reservoir about were10.04 about× 108 m 10.03/a, accounting× 10 m /a,for accounting6.08% of average for 6.08% annual of average streamflow annual in the streamflow MRYR basin in the [17 MRYR]. basin [17].

Water 2018, 10, 1231 16 of 20 Water 2018, 10, x FOR PEER REVIEW 16 of 20

90 Longyangxia ) 70 3

m Liujiaxia

8 50 Xiaolangdi 30 10 -10 variation (10 1985 1990 1995 2000 2005 2010 2015 2020

Annual storagecapacity -30 -50 Year FigureFigure 16. Variations16. Variations of annual of annual storage storage capacity capacity of Longyangxia, of Longyangxia, Liujiaxia Liujiaxia and and Xiaolangdi Xiaolangdi reservoirs reservoirs alongalong the the Yellow Yellow River. River.

(3) (3)Effect Effect of soilof soil and and water water conservation conservation SinceSince the the 1950s, 1950s, a number a number of soilof soil conservation conservation measures measures were were implemented implemented on on the the Loess Loess Plateau, Plateau, includingincluding terraces, terraces, check check dams, dams, reservoirs, reservoirs, afforestation afforestation and and planting planting grass grass [11]. [11 By]. By the the end end of 2007,of 2007, thethe preliminary preliminary area area of soilof soil and and water water conservation conservation was was 225.6 225.6× 10 × 910m9 2m,2 including, including 119.5 119.5× ×10 109 9m m22 of of forestationforestationand and 36.7 36.7× ×10 109 9m m22 ofof land land terracing terracing [7 [].7]. These These measures measures have have significantly significantly altered altered the the hydrologicalhydrological regime regime of the of river the river [53]. Ref.[53]. [17 Ref.] concluded [17] concluded that streamflow that streamflow reduction reduction through soilthrough and water soil and conservationwater conse measuresrvation respectivelymeasures respectively 4.54 × 108 m 4.543/a, × 5.70 108 ×m310/a,8 5.70m3/a × and108 m 6.413/a ×and10 6.418 m3 /a× 10 in8 1970–1979,m3/a in 1970– 1980–19891979, 1980 and– 1990–19961989 and periods. 1990–1996 Ref. periods. [55] pointed Ref. out[55 that] pointed water and out soil that conservation water and measures soil conservation have becomemeasures the main have influencing become the factor main for influencing the dramatic factor reduction for the of dramatic water in thereduction middle of reaches. water in the middle reaches. 5.2. Analysis of Change Point 5.2The. Analysis variation of Change of flow Point occurred at the Tangnaihai mainly around 1989. In the headwaters of the YellowThe River, variation abrupt of flow changes occurred occurred at the in Tangnaihai the mid-1980s mainly for around temperature, 1989. In in the the headwaters late 1980s forof the precipitationYellow River, and in abrupt the early changes 1980s occurredfor sunshine in durationthe mid-1980s and pan for evaporation temperature, [43 in]. the late 1980s for precipitationFor the other and 9 stations, in the early the variation 1980s for points sunshine all were duration around and 1985 pan in thisevaporation study. However, [43]. in previous research,For there the are other different 9 stations, abrupt the points. variation In middle points reaches all were of the around Yellow 1985 River, in Ref.this [ 17study.] indicated However that, in theprevious abrupt decline research, in streamflow there are different began in abrupt 1985. Ref. points. [19] concludedIn middle thatreaches the abruptof the Yellow change ofRiver, the waterRef. [17] dischargeindicated from that the the late abrupt 1980s todecline the early in streamflow 1990s arose began fromhuman in 1985. extraction. Ref. [19] concluded Ref. [51] suggested that the thatabrupt abruptchange changes of the in water streamflow discharge at mainstream from the late stations 1980s occurredto the early when 1990s large arose reservoirs from human were built,extraction. which Ref. agrees[51] with suggested this study. that abrupt changes in streamflow at mainstream stations occurred when large reservoirs were built, which agrees with this study. 5.3. Analysis of Period 5.3It. isAnalysis a very of difficult Period to estimate periods in hydrologic series effectively [24]. Ref. [50] found that there areIt significantis a very difficult periods to at estimate scales of periods quasi-biannual, in hydrologic 4 years, series 6–8 effectively years, 12–14 [24]. yearsRef. [50 and] found above that 20 yearsthere are inboth significant the flow periods and regional at scales climateof quasi variations-biannual, in4 year thes source, 6–8 years region, 12 of–14 the years Yellow and River.above 20 Ref.year [33]s studiedin both the streamflowflow and regional periodicity climate at the variations Guide station, in the source and within region a 52-yearof the Yellow scale detected River. Ref. circa[33 8] yearsstudied and the 17 streamflow years periodicity periodicity for theat the observed Guide station, streamflow. and within Some a periods 52-year of scale this detected study are circa consistent8 years with and these 17 years studies. periodicity for the observed streamflow. Some periods of this study are consistentFor Toudaoguai with these station, studies. thequantitative periodicity of the observed streamflow is located at circa 4, 8, andFor 18 yearsToudaoguai within thestation, 52-year the period. quantitative For the periodicity Huayuankou of the station observed in the streamflow observed streamflow, is located at therecirca are 4,circa 8, 3,and 9 and 18 21years year within periods the within 52-year the 52 period. year period. For the The Huayuankou observed streamflows station in at the the observed Lijin stationstreamflow, had the same there main are periodicitycirca 3, 9 and of 3, 21 9, and year 21 periods years for within the observed the 52 streamflow year period. [33 The]. Ref. observed [24] foundstreamflows that the periods at the Lijin of annual station flow had datathe same at the main Lijin periodicity hydrologic of station 3, 9, and are 1821 years,years for 11 yearsthe observed and 3 years.streamflow In this paper,[33]. Ref. the [ main24] found periodicities that the wereperiods 30, of 21 annual and 16 yearsflow data within at the 56Lijin year hydrologic period. station are 18 years, 11 years and 3 years. In this paper, the main periodicities were 30, 21 and 16 years within the 56 year period.

Water 2018, 10, 1231 17 of 20

Annual flow is an important index of a river, which may be affected by solar activities. The relationships between solar activities and the flow in the Yellow River are complicated [9]. The correlation between sunspot relative number and flow may be lower, because the flow progress is affected by factors such as precipitation, land cover and land use, soil, vegetation, and others besides solar activities. The climatic variability exists in all the data types and is partially coincident with known climate cycles such as the Pacific Decadal Oscillation and the EI Nino-Southern Oscillation [56]. Ref. [47] found that in the middle and lower reaches of the Yellow River, the cycle of precipitation is linked with El Nino events, Wolf Sun Spot Numbers and Pacific Decadal Oscillation (PDO). Ref. [12] didn’t detect periodicity from 1969 to 1973 and after 1986 in his study. He suggested this can be attributed to the intensive human activities in the upper and middle reaches of the Yellow River during the past six decades. In summary, the effects of the aforementioned factors on flow periodicity are associated with a number of complex physical processes. More robust methods are thus desired to be advanced in the future studies.

6. Conclusions First, the runoff trends of the upper, middle and lower stream along the Yellow River are decreasing. The reductions of annual runoff are more and more from the upstream to the downstream of the Yellow River since 1986. These changes are mainly influenced by human activities, especially water extraction and diversion and the operations of the large reservoirs. Second, the abrupt change points of the stations other than Tangnaihai happened in 1985 which is relative to the construction of the large reservoirs. Third, the flows along the Yellow River present characteristics of multiscale changes. At the large scale, the periodic changes were very stable for the whole period. At the small scale, the periodic changes were local and complicated. The first main period of runoff in the Yellow River is 30 years, and the second main period is 15 years in most of the upper and the middle reaches and 20.67 years in the lower reaches. Therefore, the water resources crisis in the Yellow River basin is very severe. In order to ensure sustainable use of the water resources of the Yellow River, some effective measures must be taken, such as further developing the scientific operations of reservoirs, reasonable allocation of water resources and implantation of necessary water-saving measures, especially in agriculture.

Author Contributions: X.W. designed the article structure and wrote the manuscript; B.E. initiated the idea of this review article and revised the manuscript; X.Y. provided important academic guidance; P.Y. contributed to the discussion of the results and to the analysis. All authors reviewed the manuscript. Funding: This research is funded by the National Key R&D Program of China (2017YFC0405601), the National Scholarship Council and the Science Fund for Creative Research Groups of the National Natural Science Foundation of China (51621092). Acknowledgments: We thank all the authors who contribute to this special issue. We thank the reviewers who contributed their expertise and time on reviewing those articles. Without their support, it would be impossible to assess properly those manuscripts submitted. We thank the Yellow River commission for providing valuable data. Conflicts of Interest: The authors declare no conflict of interest.

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